Filip Rooms

Estimating image blur in the wavelet domain

A fast and efficient hybrid block-matching motion estimation algorithm is proposed in this paper. The fact that motion characteristics are relatively stable in a GOP (group of pictures) is utilized to reduce the computation burden without quality degradation. For P-frames, a multi-resolution block-matching algorithm is used. At the coarsest level, edge information is used to reduce searching positions before an exhaustive search. At the finer levels, only the several candidates chosen at the upper level are verified. For B-frames, MAE (mean absolute error) is used not only for the matching criteria, but also for classifying the macroblocks with the motion information extracted from P-frames. Concurrently, the search window size is adjusted using the contribution of different motion range to the improvement of the picture quality with motion compensation. When the decoded I-frame and P-frames are used as the reference, experimental results show that reconstructed digital frames have even better quality than the full search algorithm in terms of PSNR, under MPEG-1 coding environment.In this paper, a wavelet based method is proposed to estimate the blur in an image using information contained in the image itself. We look at the sharpness of the sharpest edges in the blurred image, which contain information about the blurring. Specifically, a smoothness measure, the Lipschitz exponent, is computed for these sharpest edges. Its value is related to the variance of a gaussian point spread function. This value is only dependent on the blur in the image and not on the image contents. This allows us to estimate the variance of the blur directly from the image itself. With minor modifications, the method can be extended to other types of blur that are described by one parameter (airy disks, out-of-focus blur, …).

Evaluation of the wear label description in carpets by using local binary pattern techniques

Carpet manufacturers certify their products for end-use applications by evaluating the wear behavior of their carpets in mechanical experiments. Currently, this process is performed by visual inspection, suffering from subjective gathers that limit reliability. To automate this process, we propose the use of image processing techniques, specifically of local binary pattern (LBP) statistics. Such statistics are tolerant against illumination changes, can be easily implemented, and perform well when combined with a symmetrized adaptation of the Kullback—Leibler divergence. As a main innovation, we extend the existing rotationally invariant LBPs by including ‘mirror’ and ‘complement’ invariants. We show an accurately improved and more reliable estimation of the degree of wear in worn carpets. The evaluation is performed on four digital reference scales, each containing eight pairs of images comparing transitional degrees of wear to the original appearance. Additionally, the texture changes due to distortions of the pile yarn tufts are enhanced by choosing a suitable scale factor per reference. We validate the findings using six physical reference scales, each containing four pairs of images. In both references, linear correlations of over 0.89 are demonstrated between the degrees of wear and extracted features from the images. These findings justify the use of the proposed LBP extensions in a first approach towards an automated low-cost inspection system for carpet wear at low computation cost.

Simultaneous degradation estimation and restoration of confocal images and performance evaluation by colocalization analysis

A novel method for joint restoration and estimation of the degradation of confocal microscope images is presented. The observed images are degraded due to two sources: blurring due to the band‐limited nature of the optical system [modelled by the point spread function (PSF)], and Poisson noise contaminates the observations due to the discrete nature of the photon detection process. The proposed method iterates noise reduction, blur estimation and deblurring, and applies these steps in two phases, i.e. a training phase and a restoration phase. In the first phase, these three steps are iterated until the blur estimation converges. Noise reduction and blur estimation are performed using steerable pyramids, and the deblurring is performed by the Richardson–Lucy algorithm. The second phase is the actual restoration. From then on, the blur estimation is used as a criterion to measure the image quality. The iterations are stopped when this measure converges, a result that is guaranteed. The integrated method is completely automatic, and no prior information on the image is required. The method has been given the name SPERRIL (Steerable Pyramid‐based Estimation and Regularized Richardson–Lucy restoration). Compared with existing techniques by both objective measures and visual observation, in the SPERRIL‐restored images noise is better suppressed.

Feature Extraction of the Wear Label of Carpets by Using a Novel 3D Scanner

In this paper we present a novel 3D scanner to capture the texture characteristics of worn carpets into images of the depth. We first compare our proposed scanner to a Metris scanner previously attempted for this application. Then, we scan the surface of samples from the standard EN1471 using our proposed scanner. We found that our proposed scanner offers additional benefits because it has been specifically designed for carpets, performing faster, cheaper, better and also a lot more suitable for darker carpets. The results of this approach give optimistic expectations in the automation of the label assessment dealing with multiple types of carpets.

Parametric PSF estimation via sparseness maximization in the wavelet domain

Image degradation is a frequently encountered problem in different imaging systems, like microscopy, astronomy, digital photography, etc. The degradation is usually modeled as a convolution with a blurring kernel (or Point Spread Function, psf) followed by noise addition. Based on the combined knowledge about the image degradation and the statistical features of the original images, one is able to compensate at least partially for the degradation using so-called image restoration algorithms and thus retrieve information hidden for the observer. One problem is that often this blurring kernel is unknown, and has to be estimated before actual image restoration can be performed. In this work, we assume that the psf can be modeled by a function with a single parameter, and we estimate the value of this parameter. As an example of such a single-parametric psf, we have used a Gaussian. However, the method is generic and can be applied to account for more realistic degradations, like optical defocus, etc.

Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant.

Efficient blur estimation using multi-scale quadrature filters

Blur estimation is required in image processing techniques such as auto-focussing, quality assessment for compressed images and image fusion. In this paper a multi-scale local blur estimation method is proposed. We use the energy of a pair of quadrature filters with first and second derivatives of a Gaussian at several scales as its constituents. A new strategy for analyzing the extrema of energy across scale is proposed. Comparing to the methods which use just a Gaussian first derivative kernel, a smaller number of scales needs to be processed. Also our method yields only one response at the centroid of line-shape features at a position independent of the scale. This is in contrast to other methods which yield multiple responses at scale dependent positions. We evaluated the method for synthetic and real images from the LIVE database. Depending on details of the image, the proposed method is several to tens of times faster in comparison with using just a Gaussian first derivative. The accuracy of the blur estimation is found to be the best or second best while comparing with 16 different methods for Gaussian blur. In addition, the performance is still good for motion blurred images.

Integrated approach for estimation and restoration of photon-limited images based on steerable pyramids

In disciplines like fluorescence microscopy and astronomical imaging, the imaging process is based on detection of photons. Fluctuations in photon counting processes are described by Poisson statistics. In this paper, a new combined method based on steerable pyramids is proposed for the estimation of the degradation parameters (like noise and blur) and the restoration of photon-limited images. It consists of the following steps: in the steerable pyramid domain, a noise suppression step is performed, followed by a blur estimation. As a last step, the Richardson-Lucy deconvolution algorithm is applied. These steps are iterated. The only free parameter in the algorithm is the number of iterations, but an empirical stopping rule is suggested in terms of the blur estimation. Otherwise, this method is fully automatic and provides very nice restoration results.

Image fusion using blur estimation

In this paper, a new wavelet based image fusion method is proposed. In this method, the blur levels of the edge points are estimated for every slice in the stack of images. Then from corresponding edge points in different slices, the sharpest one is brought to the final image and others are eliminated. The intensities of non-edge pixels are assigned by the slice of its nearest neighbor edge. Results are promising and outperform other methods in most cases of the tested methods.

Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images

Carpet customers want a product of which the appearance lasts for years. Therefore, carpet manufacturers certify their products with labels that represent the expected change in appearance after the first year of installation. No automated system exists yet for objectively assigning these ranks. In this approach, we present an automated method for assessing carpet wear based on image analysis. For this, depth and intensity information are captured from eight types of carpet samples. The results show that the method correctly assigns wear labels from 1 to 5 in steps of 1 for six of the eight carpet types.